library(tidyverse)
library(kableExtra)
library(magrittr)
library(plotly)
library(sf)

This is a very basic static webpage you can make from an .Rmd file.

Tiny examples

Rmarkdown

Math expressions, e.g. \(Y = X\beta + \epsilon\).

You can insert footnotes.1

Make links to important places like a Github Repository for this static webpage you are viewing.

Tables

There are excellent ways to display tabular data.

One of my favorite resources here.

Sepal.Length Sepal.Width Petal.Length Petal.Width Species
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa
4.6 3.4 1.4 0.3 setosa
5.0 3.4 1.5 0.2 setosa
4.4 2.9 1.4 0.2 setosa
4.9 3.1 1.5 0.1 setosa

You can get very over-the-top with different ways to organize the data.

Maybe Too Many Groups
This Group
That Group
My Group
My Other Group
Another Group
mpg cyl disp hp drat wt
Mazda RX4 21.0 6 160 110 3.90 2.620
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875
Datsun 710 22.8 4 108 93 3.85 2.320
Hornet 4 Drive 21.4 6 258 110 3.08 3.215
Hornet Sportabout 18.7 8 360 175 3.15 3.440

Plots

R has amazing graphing powers—my favorite is the ggplot2 package—and a great community with people sharing their work—look up #TidyTuesday on Twitter—and teaching others how to do great work too. The plotly package let’s you make your plots interactive as well. Below are some examples from the Plotly reference guide, shown first as static images and then as interactive ones.

nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"), quiet = TRUE)


nc_plot <- ggplot(data = nc) + geom_sf(aes(fill = AREA))
nc_plot

fig <- ggplotly(nc_plot)

fig
set.seed(8)
dat <- data.frame(cond = factor(rep(c("Treatment","Placebo"), each=200)), happiness = c(rnorm(200),rnorm(200, mean=.8)))

p <- ggplot(dat, aes(x=cond, y=happiness, fill=cond)) + geom_boxplot()

p

fig <- ggplotly(p)

fig
df <- diamonds[sample(1:nrow(diamonds), size = 1000),]

p <- ggplot(df, aes(x = color)) + 
  geom_bar(aes(y = ..count../sum(..count..), fill = cut)) + 
  scale_fill_brewer(palette = "Set3") + 
  ylab("Percent") + 
  ggtitle("Show precentages in bar chart") +
  labs(x = "Color")

p

fig <- ggplotly(p)

fig

  1. A very informative footnote here.↩︎